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OPINION: The world is changing fast. Students need data science instruction ASAP

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Netflix machine-learning algorithms, for example, leverage rich user data not just to recommend movies, but to decide which new films to make. Generative AI algorithms, like those used to create ChatGPT, train on large language datasets. Facial recognition software deploys neural nets to leverage pixel data from millions of images.

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The Meeting of the Minds That Launched AI

Hacker News

Organized by John McCarthy , Marvin Minsky , Claude Shannon , and Nathaniel Rochester , it brought together a few dozen of the leading thinkers in AI, computer science, and information theory to map out future paths for investigation. A group photo [shown above] captured seven of the main participants. Where Was the Photo Taken?

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Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk

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The analysis included 218 patients admitted to Qilu Hospital of Shandong University from July 2011 to April 2024. Feature selection via the Boruta and LASSO algorithms preceded the construction of predictive models using Random Forest, Decision Tree, K-Nearest Neighbors, Support Vector Machine, LightGBM, and XGBoost.

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Amazon EC2 P5e instances are generally available

AWS Machine Learning Blog

Customers using dynamic programming (DP) algorithms for applications like genome sequencing or accelerated data analytics can also see further benefit from P5e through support for the DPX instruction set. Before joining AWS, Aman graduated from Rice University with degrees in Computer Science, Mathematics, and Entrepreneurship.

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What Is a Transformer Model?

Hacker News

Attention Net didn’t sound very exciting,” said Vaswani, who started working with neural nets in 2011.Jakob Their Bidirectional Encoder Representations from Transformers ( BERT ) model set 11 new records and became part of the algorithm behind Google search.

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What Is ChatGPT Doing … and Why Does It Work?

Hacker News

And in fact the big breakthrough in “deep learning” that occurred around 2011 was associated with the discovery that in some sense it can be easier to do (at least approximate) minimization when there are lots of weights involved than when there are fairly few. But there’s something potentially confusing about all of this.

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You need much less memory than time

Hacker News

Williams proof relies on a space-efficient tree evaluation algorithm by James Cook and Ian Mertz from last years STOC conference. Cook and Mertzs algorithm builds on earlier work on catalytic computing, highlighted in a recent Quanta article. Williams then applies the tree evaluation algorithm of Cook and Mertz.

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